Small molecule structure elucidation from NMR spectra with generative and reasoning models

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Abstract
Elucidating the structures of unknown small molecules remains a major challenge in chemistry, pharmacology, and drug discovery. While nuclear magnetic resonance (NMR) spectroscopy is the gold standard for structure elucidation, interpreting spectra remains labor-intensive and requires expert knowledge to navigate the vast space of possible structures. Here, we present an AI system for rapid, automated de novo structure elucidation that combines a molecular generative model with a scientific reasoning agent. Its generative component, C hef NMR2, is an atomic flow-matching model that predicts small-molecule structures from molecular formulae and one-dimensional 1 H and 13 C NMR spectra. ChefNMR2 - Agen t augments our generative model with large language models (LLMs) to reason over and refine candidate structures. Across diverse benchmarks, ChefNMR2 substantially outperforms existing tools, offering calibrated confidence estimates and two to three orders of magnitude faster inference than frontier LLMs. We use ChefNMR2 - Agen t to elucidate the structures of four novel natural products, matching human-chemist accuracy in a fraction of the time. Our work demonstrates how specialized generative models and general-purpose reasoning agents can unite to solve challenging scientific inverse problems and accelerate NMR structure elucidation, thus opening new possibilities for AI-driven small-molecule discovery across diverse disciplines.
Funding Information
  • Eric and Wendy Schmidt Transformative Technology Fund
  • Princeton SEAS Innovation Grant
  • Anthropic‘s AI for Science Program
  • Princeton Catalysis Initiative
  • National Research Foundation of Korea (RS-2024-00406578)

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